Accountable for the end-to-end architecture, engineering blueprint, deployment model,

operational readiness, security, governance and integration strategy of client’s enterprise AI systems — ensuring that AI solutions operate as secure, scalable, compliant and business-

aligned systems across models, applications, infrastructure, data, networks and external dependencies.

That means the architect will have visibility across the entire chain:

Business → Business Rules → AI Platform → Models → Data/Knowledge grpahs →

APIs/Integration → Network → Infrastructure → Security → Deployment → Operations →

Monitoring

Key responsibilities

1. End-to-end AI system architecture

 Define the overall architecture of AI systems across models, agents, applications, data, APIs, infrastructure and external services.

 Establish architecture principles, reference architectures and technology standards.

 Define the technology selection principles and coding standards

 Define the architecture standards to be followed by Individual AI products

 Establish

 Ensure architecture supports scalability, resilience, performance and maintainability.

1. Infrastructure & deployment

 Define/ Approve the deployment architecture across cloud/ on-premise/ hybrid environments both for Maveric and client environments  Define/ Approve requirements for Kubernetes, GPU infrastructure, storage,

networking and compute.

 Establish/ Approve deployment, release and rollback patterns for AI systems.

 Define checklists and guidelines to ensure production-readiness of AI products.

1. AI engineering ecosystem development





 Define the standardized and reusable capabilities such as the following that needs to be consumed by all Maveric AI products in a standardized manner o Foundation models o AI gateways o Model routing o Vector databases o Prompt management o RAG o Guardrails o Observability o AI security  Define how Maveric AI platforms consume centralized platform capabilities

 Work with Delivery and Integration leader to create the reusable services

 Determine what should be centralized as a platform capability versus embedded within individual AI Products.

1. Business rules & AI controls

 Ensure business rules, policies, decision logic and human-in-the-loop controls are properly incorporated into the Maveric AI Platforms.  Define boundaries between LLM/model behaviour and deterministic business logic

(i.e what goes to LLM vs. What is not going to LLM)

 Define the model selction guidelines

 Ensure AI products follows the architecture patterns in a manner its outputs can be controlled, validated and audited.

1. Integration & external ecosystem

 Own the architectural integration of AI systems with: o Core banking / enterprise applications o APIs o Identity platforms o Data platforms o External AI/model providers o Third-party services o Enterprise networks

 Assess dependencies and architectural risks associated with external providers.

1. Security, risk & compliance





 Ensure AI architecture incorporates security and regulatory requirements.  Define controls for data privacy, model security, prompt injection, data leakage,

access control and model abuse.

 Work with Cybersecurity, Risk, Legal and Compliance teams to establish AI controls.

 Ensure appropriate auditability and traceability is defined and implemented

1. Reliability & operations

 Define SLOs, RTO/RPO and operational readiness requirements.
1. Technology & vendor strategy

 Evaluate AI technologies, models, platforms and vendors.  Define technology selection criteria and enterprise standards.

 Degine standards for opensource tool stack selection

 Approve opensource tools before they are deployed in Maveric workplace

 Establish technology lifecycle and obsolescence strategy.

1. Architecture governance

 Review and approve AI solution architectures.  Establish architecture review checkpoints.

 Maintain enterprise AI reference architecture and standards.

 Identify and manage technical debt and architectural risks.

Experience Range

Someone with 20+ yrs of experience with enterprise architecture having hands-on experience in architecting and Implementing AI platforms and solutions

Typical Level – AVP

Typical skills The role is for someone who is T-shaped, rather than an expert only in AI.

Core

 Enterprise architecture

 AI governance

 AI/ML architecture

 Generative AI / LLMs / Agents

 Cloud & hybrid architecture

 Kubernetes / containers

 APIs & integration

 Data architecture

 Networking

 Cybersecurity

 DevSecOps / CI-CD

 Observability / SRE

📌 Sr. Enterprise AI Architect [18+yrs] (Bengaluru)
🏢 Nasugroup.com
📍 Bengaluru